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Liver biopsy is considered to be the gold standard for analyzing chronic hepatitis and fibrosis; however, it is an invasive and expensive approach, which is also difficult to standardize. Medical imaging techniques such as ultrasonography, computed tomography (CT), and magnetic resonance imaging are non-invasive and helpful methods to interpret liver texture, and may be good alternatives to needle biopsy. Recently, instead of visual inspection of these images, computer-aided image analysis based approaches have become more popular. In this study, a non-invasive, low-cost and relatively accurate method was developed to determine liver fibrosis stage by analyzing some texture features of liver CT images. In this approach, some suitable regions of interests were selected on CT images and a comprehensive set of texture features were obtained from these regions using different methods, such as Gray Level Co-occurrence matrix (GLCM), Laws’ method, Discrete Wavelet Transform (DWT), and Gabor filters. Afterwards, sequential floating forward selection and exhaustive search methods were used in various combinations for the selection of most discriminating features. Finally, those selected texture features were classified using two methods, namely, Support Vector Machines (SVM) and k -nearest neighbors ( k -NN). The mean classification accuracy in pairwise group comparisons was approximately 95% for both classification methods using only 5 features. Also, performance of our approach in classifying liver fibrosis stage of subjects in the test set into 7 possible stages was investigated. In this case, both SVM and k -NN methods have returned relatively low classification accuracies. Our pairwise group classification results showed that DWT, Gabor, GLCM, and Laws’ texture features were more successful than the others; as such features extracted from these methods were used in the feature fusion process. Fusing features from these better performing families further improved the classification performance. The results show that our approach can be used as a decision support system in especially pairwise fibrosis stage comparisons. [ABSTRACT FROM AUTHOR]

In this study, the application of a backscattering imaging system with different approaches of transform-based image texture analysis for the evaluation of banana quality at different ripening stages was investigated with Wavelet, Gabor and Tamura transforms. The attenuated images of the fruits were acquired using Laser Light Backscattering Imaging (LLBI) with laser diodes emitting light at three wavelengths viz 532, 660, and 830 nm. The elasticity, chlorophyll index and soluble solids content (SSC) of each sample were measured as reference parameters by using a texture analyser, a Delta Absorbance (DA) meter, and a refractometer, respectively. The performance of the extracted features from the selected transform-based image texture analysis for analysing the quality parameters of the fruit was evaluated by means of an artificial neural network (ANN) and a support vector machine (SVM). The results indicated that there were significant changes of elasticity, chlorophyll index and SSC as the ripening stages increased. Prediction model analysis showed that the Wavelet transform exhibited the most reliable results for all of the reference parameters followed by Tamura and the Gabor transform. The results also revealed that analysis using an ANN approach recorded better performance than SVM as reflected by higher coefficient of determination (R 2 ) values. Thus, this study indicated that an LLBI system with transform-based image texture analysis coupled with computational intelligence techniques can be used for the evaluation of the quality of bananas. [ABSTRACT FROM AUTHOR]

The paper presents a new image classification technique which first extracts rotation-invariant image texture features in singular value decomposition (SVD) and discrete wavelet transform (DWT) domains. Subsequently, it exploits a support vector machine (SVM) to perform image texture classification. For convenience, it is called the SRITCSD method hereafter. First, the method applies the SVD to enhance image textures of an image. Then, it extracts the texture features in the DWT domain of the SVD version of the image. Also, the SRITCSD method employs the SVM to serve as a multiclassifier for image texture features. Meanwhile, the particle swarm optimization (PSO) algorithm is utilized to optimize the SRITCSD method, which is exploited to select a nearly optimal combination of features and a set of parameters utilized in the SVM. The experimental results demonstrate that the SRITCSD method can achieve satisfying results and outperform other existing methods under considerations here. [ABSTRACT FROM AUTHOR]

Source camera identification enables forensic investigator to discover the probable source model that are employed to acquire the image under investigation. It is important whenever digital content is presented as a silent witness. In this paper, we present a source camera identification method via image texture features that are extracted from well selected color model and color channel. Except to distinguish source camera models from images whatever they are captured via same or different brand cameras, the main contributions of the proposed method are as follows: (1) It can distinguish imaging device individuals from images even if they are taken by using same brand and model of devices. (2) It is robust for content-preserving manipulations or geometric distortions, such as JEPG compression, adding noise, and rotation and scaling. The experimental results demonstrate that the performance of the proposed method is satisfactory. Compared with the state-of-the-art methods, the proposed method is superior in both detection accuracy and robustness. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this study was to investigate the usefulness of raw meat surface characteristics (texture) in predicting cooked beef tenderness. Color and multispectral texture features, including 4 different wavelengths and 217 image texture features, were extracted from 2 laboratory-based multispectral camera imaging systems. Steaks were segregated into tough and tender classification groups based on Warner–Bratzler shear force. The texture features were submitted to STEPWISE multiple regression and support vector machine (SVM) analyses to establish prediction models for beef tenderness. A subsample (80%) of tender or tough classified steaks were used to train models which were then validated on the remaining (20%) test steaks. For color images, the SVM model correctly identified tender steaks with 100% accurately while the STEPWISE equation identified 94.9% of the tender steaks correctly. For multispectral images, the SVM model predicted 91% and STEPWISE predicted 87% average accuracy of beef tender. [Copyright &y& Elsevier]

Abstract: Motivation: Physical properties of coal such as particle size distribution have a large influence on the stability and operational behavior of fluidized bed reactors and metallurgical furnaces. In particular, the presence of large amounts of “fine” particles invariably has a drastic effect on plant performance as a result of impaired gas permeability characteristics of the coal or ore burden. Therefore, monitoring and control of particle size distribution profiles of such aggregate material on reactor feed streams, such as moving conveyor belts, is critical for predictable operation of these processes. Traditionally, the method of sieve analysis using stock or belt cut samples has been widely used in industry. Unfortunately, the reliability and usefulness of belt cut techniques are constrained by frequency of sampling as well as laboratory analysis turnaround times. For real-time monitoring and control purposes, automated sampling and analysis methods are more desirable. Methods: In this study, the problem of estimating the particle size distribution profile of material on a moving conveyor belt is formulated within a texture classification framework, which has its basis in machine vision and incorporates elements from statistical pattern recognition. Using exemplar images of coal particles taken on a process stream, a set of local features that compactly describes the textural properties of each image are expressed in terms of localized nonlinear features called textons. Representation of image information using textons is primarily motivated by insights from neuroscience research on the optimality of linear oriented basis functions as models of perception in early processing of visual information in the cortex regions of the human brain. Using these representations for different textures, nearest neighbor and support vector machine classification models are subsequently used to classify test images. Results: Using a comprehensive evaluation, it is shown that the use of texton representation obtained from decomposing images with linear oriented basis functions can be sufficiently discriminative compared to the use of the widely used second-order statistical features or features from other baseline models. In particular, model performance obtained with appropriately tuned filters suggest the importance of including shape and spatial structure information in an image representation for texture classification of coal particles. Furthermore, using nonlinear support vector machines rather than nearest neighbor classifiers significantly improved classification performance. A texture classification approach to particle size profile estimation has potential applications in the online monitoring of the proportion of “fines” in coal material on moving conveyor belts. [Copyright &y& Elsevier]

Fault detection and diagnosis of its severity for machine health monitoring can be stated as a nested classification problem. For a faulty bearing, each fault location whether belonging to inner race, outer race or the ball can be seen as multiclass classification with three classes while the varying degree of severity in each class can be viewed as a sub classification task. The peculiar vibration patterns generated from the flaws in different bearing parts and with varying degree of distortion can be classified into various classes and subclasses for analysis of vibration signatures. This paper proposes a multiclass support vector machines (MSVMs) based fault classification approach for fault diagnosis of ball bearings. The one dimensional vibration signals are converted to two dimensional gray scale images resulting in textural patterns which are then enhanced using the wave atom transform. Features such as semivariance, skewness and entropy are extracted from the texture images and the MSVM is then trained using feature matrices generated from feature vectors. The MSVM is trained in two phases; in the first phase, the classifier categorizes the location of the fault and in the second phase the classifier does the diagnosis regarding the size of the fault at that particular location. Simulation results show that the proposed technique is highly robust in locating the fault and its severity. [ABSTRACT FROM AUTHOR]

• An automatic method for pavement patch detection is proposed. • Image processing is used to compute pavement image texture. • Least Squares Support Vector Machine is used for data classification. • Differential Flower Pollination is employed for model optimization. • The new model can achieve a good prediction result (CAR = 95%). Patch detection is an important task in pavement condition survey. This study establishes an automatic approach for asphalt pavement patch recognition based on image texture analysis and hybrid machine learning algorithms. Features based on image texture that employs statistical properties of color channels and the gray-scale co-occurrence matrix are used by the Least Squares Support Vector Machine (LSSVM) for discriminating patched areas from non-patch ones. In addition, to optimize the LSSVM training phase, the Differential Flower Pollination (DFP) metaheuristic is used. A data set constructed from a set of 1000 image samples has been utilized to train and verify the proposed integration of image texture analysis techniques, LSSVM, and DFP. Experimental results show that the new model can achieve a good prediction result with Classification Accuracy Rate = 95.30%, Positive Predictive Value = 0.96, and the Negative Predictive Value = 0.95. Additionally, a patch detection program has been developed and compiled in Visual C#.NET to ease the implementation of the hybrid model. Thus, the newly developed method can be a potential tool for traffic management agencies during the phase of pavement condition evaluation. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • SVM-based classification of steelusing textural features and morphological parameters. • A combination of different parameter groups leads to the highest classification result. • Significant parameters could show the same range of accuracy as the whole dataset. Abstract The variety of modern steels is growing steadily. In order to meet the ever tighter tolerance ranges for the properties of these steels, it is important to both understand the manufacturing process as accurately as possible and to be able to correctly classify the microstructure. The microstructure acts as a link between the production and the properties, which acts as an information storage, which must be read out and understood in the best way to develop new steels. For this reason, it is of utmost importance to have an objective and reproducible microstructure classification available. The present study demonstrates that using a support vector machine in combination with pixel-based and morphology-based parameters allows a reliable classification based on microstructural images. In order to determine the parameters correlative microscopy is used to collect a large variety of information about the different steel structures. The significance of the different parameter groups for the classification success and the correlation of the parameters with each other are investigated. The minimum number of parameters required for a reliable classification is determined by evolutionary feature selection. [ABSTRACT FROM AUTHOR]

• Cotton water status was estimated using vegetation indices and textural information. • Combining vegetation indices with textural information enhanced estimation accuracy. • The XGBoost_VIs + TFs + TIs model performed the best in estimating CWSI. • The estimated CWSI predictive map could diagnose cotton canopy water stress. Precise and timely estimation of crop canopy water status is of great importance for precision irrigation. The unmanned aerial vehicle (UAV)-based remote sensing technology has become increasingly popular for crop canopy water status estimation. Nevertheless, the capability of vegetation indices (VIs) along with textural information from high-resolution imagery for estimating cotton canopy water status has been rarely explored. The VIs, texture features (TFs), and texture indices (TIs) were obtained from UAV multispectral images of cotton with different irrigation levels and nitrogen rates in the southern Xinjiang of China. The performances of three machine learning models, i.e. support vector machine (SVM), back-propagation neural network (BPNN) and extreme gradient boosting (XGBoost) were evaluated for estimating canopy equivalent water thickness (CEWT) and crop water stress index (CWSI) throughout the cotton growing season. The results showed that combining vegetation indices and textural information significantly improved the estimation accuracy of models compared to vegetation indices or textural information alone. The XGBoost_VIs + TFs model exhibited the best accuracy in estimating CEWT (R2 = 0.75, RMSE = 0.01 cm, RE = 19.46 % at upper half-canopy level, and R2 = 0.65, RMSE = 0.02 cm, RE = 24.59 % at all-canopy level), while the XGBoost_VIs + TFs + TIs model performed best in predicting CWSI among the models (R2 = 0.90, RMSE = 0.05, RE = 5.84 %). Although CEWT estimation was fair to some extent, CWSI estimation was more applicable for diagnosing cotton water stress. The CWSI maps created from the optimal XGBoost_VIs + TFs + TIs model intuitively reflected the cotton canopy water status under various irrigation levels and nitrogen rates, which could help farmers implement timely and precision irrigation in cotton production. [ABSTRACT FROM AUTHOR]

Skin melanoma is a potentially life-threatening cancer. Once it has metastasized, it may cause severe disability and death. Therefore, early diagnosis is important to improve the conditions and outcomes for patients. The disease can be diagnosed based on Digital-Dermoscopy (DD) images. In this study, we propose an original and novel Automated Skin-Melanoma Detection (ASMD) system with Melanoma-Index (MI). The system incorporates image pre-processing, Bi-dimensional Empirical Mode Decomposition (BEMD), image texture enhancement, entropy and energy feature mining, as well as binary classification. The system design has been guided by feature ranking, with Student's t-test and other statistical methods used for quality assessment. The proposed ASMD was employed to examine 600 benign and 600 DD malignant images from benchmark databases. Our classification performance assessment indicates that the combination of Support Vector Machine (SVM) and Radial Basis Function (RBF) offers a classification accuracy of greater than 97.50%. Motivated by these classification results, we also formulated a clinically relevant MI using the dominant entropy features. Our proposed index can assist dermatologists to track multiple information-bearing features, thereby increasing the confidence with which a diagnosis is given. [ABSTRACT FROM AUTHOR]

Uranium ore concentrates (UOCs) are produced in the early stages of the nuclear fuel cycle, prior to conversion to uranium hexafluoride. Because of their high uranium content and the large-scale production, UOCs diversion from civilian use and proliferation are potential risks. This implies the necessity to develop methods able to recognise characteristic parameters correlating each UOC powder to its history and origin. Here, a novel methodology is proposed: first the reflectance spectra of 79 commercial UOCs are acquired and clustered by means of Ward's clustering analysis, then classified by Support Vector Machine (SVM). Second, SVM classification is applied to the image textural features extracted with the Grey Level Co-occurrence Matrix (GLCM) and the Angle Measure Technique (AMT) algorithms for powders in two different colour groups. The developed SVM models present good classification quality: a Matthews correlation coefficient (MCC) of 0.95 is obtained for the classification based on colours while macro-F1 is generally greater than 0.81 (MCC larger than 0.75) for the texture-based classification. These results reveal the potentiality of the present automated classification for the scopes of nuclear forensics in the identification of an unknown uranium ore concentrate sample. [Display omitted] • Illicit trafficking of uranium ore concentrates is a realistic risk. • Nuclear Forensics requires reliable methods to determine uranium ore concentrates origin. • Spectrophotometry and image texture analysis are powerful techniques for powders characterisation. • Support Vector Machine enables the identification of an unknown uranium ore concentrate sample. [ABSTRACT FROM AUTHOR]

• Texture parameters of the composites contain the information about their durability. • Machine learning algorithms used for the assessment of hydrophobization efficiency. • Microstructural properties of hydrophobic coatings modified by nanocellulose. The aim of the study was to determine a set of image texture features of the lightweight cementitious composites (LLC) with hydrophobic coatings modified with nanocellulose and use them to assess the materials' durability. A novel method based on a combination of image texture analysis and machine learning methods was proposed. Textural features were extracted from the images obtained with a scanning microscope. The best classification model was built by the Support Vector Machine method using 16 features selected by the Sequential Forward Selection algorithm. The model recognizes one of the four ranges of the contact angle, which is closely related to the degree of resistance of the analyzed material, with an accuracy of 82%. The results obtained show a relationship between the effectiveness of hydrophobic coatings in LCC and images of their surfaces. This relationship can be used with machine learning methods for conducting strength diagnostics of building materials. [ABSTRACT FROM AUTHOR]

• Asphalt paving segregation detection using image texture features. • Texture feature extraction algorithm combining uniform pattern LBP and GLCM. • Asphalt paving segregation detection based on uniform pattern LBP-GLCM. • The support vector machine classifier is used to detection whether paving segregation occurs. The segregation of asphalt pavement is the main reason for the decrease of safety, comfort and actual service life of the road, and the paving segregation is the main inducement for asphalt pavements segregation. Thus, a kind of effective paving segregation detection method can reduce the occurrence of asphalt pavement segregation. The traditional asphalt segregation detection methods are mainly divided into contact detection and non-contact detection. The contact detection method can only detect the segregation of pavement after paving or in use, and the non-contact detection method is also generally limited by the noise and expensive equipment. In recent years, the rapid development of image processing technology has provided a new research direction for asphalt paving segregation detection, but the accuracy and efficiency of the existing image-based asphalt paving segregation detection methods are insufficient. In order to solve these problems, this paper proposes an asphalt paving segregation detection method based on image texture features. Firstly, based on the traditional algorithms LBP (Local Binary Pattern) and GLCM (Gray-level Co-occurrence Matrix), a new texture feature extraction algorithm uniform pattern LBP-GLCM is proposed. Secondly, a detection method based on uniform pattern LBP-GLCM in combination with SVM (Support Vector Machine) is proposed. Then, the detection method proposed is validated using Kylbery texture dataset, the result show that this detection methods has great accuracy and efficiency in the classification of targets with similar texture features, it also means the texture feature extract method based on uniform pattern LBP-GLCM can combine the advantages of LBP and GLCM to achieve improvement of feature extraction's performance and efficiency. Finally, the detection method is applied to the diagnosis of asphalt paving segregation, and the accuracy of diagnosis achieves 94%. Compared with the existing algorithms, detection method based on uniform pattern LBP-GLCM has higher diagnostic accuracy and efficiency. Specifically, detection method with uniform pattern LBP-GLCM can improve accuracy in comparison with single asphalt pavement paving segregation detection method, and it can improve efficiency in comparison with existing hybrid asphalt pavement paving segregation detection method. The results of this study can potentially be used for real-time detection of asphalt paving segregation. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurodegenerative disease characterized by cognitive and behavioral impairment that significantly interferes with social and occupational functioning. Mild cognitive impairment (MCI) is a relatively broad clinical condition involving a slight memory deficit, which in many cases represents a transitional state between a cognitively normal (CN) condition and AD. Structural magnetic resonance (sMR) imaging has been widely used in studies related to AD because it provides images with excellent anatomical details and information about structural and contrast changes induced by the disease in the brain. Many published studies restrict their analysis to a few particular regions of the brain and search for structural changes caused by the disease. Recent studies start looking for new AD biomarkers using multiple brain regions and focusing on subtle texture changes in the image. Therefore, this study proposes a new technique for MR image classification in AD diagnosis using graph kernels constructed from texture features extracted from sMR images. In our method, we first segment the MR brain images into multiple regions with the FreeSurfer. Then, we extract 22 texture features using three methods and define the graph-node attributes as the probability distributions of the extracted features. Next, for each texture feature, we build a graph and define its edge weights as the distances between pairs of node attributes using three distance metrics. After that, we use a threshold-based approach for graph edges removal and create the graph-kernels matrices. Finally, we perform image classification using Support Vector Machines (SVMs) with two graph-kernels. Results of our method have shown better performances for the CN × AD (AUC = 0.92) and CN × MCI (AUC = 0.81) classifications, and worse for the MCI × AD case (AUC = 0.78). This trend is consistent with other published results and makes sense if we consider the concept of Alzheimer's disease continuum from pathophysiological, biomarker and clinical perspectives. Besides allowing the use of different texture attributes for the diagnosis of Alzheimer's, our method uses the graph-kernel approach to represent texture features from different regions of the brain image, which considerably facilitates the image classification task via SVMs. Our results were promising when compared to the state-of-the-art in graph-based AD classification. • Alzheimer's disease classification based on graph kernel Support Vector Machines. • Evaluation of image texture patterns of multiple brain regions in Alzheimer. • Assessment of three distance metrics to measure node attributes differences. • Threshold based method for graph edge removal to obtain discriminative graphs. • Classification results comparable to other proposed graph-based models. [ABSTRACT FROM AUTHOR]

The main purpose of this work is to explore the usefulness of fractal descriptors estimated in multi-resolution domains to characterize biomedical digital image texture. In this regard, three multi-resolution techniques are considered: the well-known discrete wavelet transform (DWT) and the empirical mode decomposition (EMD), and; the newly introduced; variational mode decomposition mode (VMD). The original image is decomposed by the DWT, EMD, and VMD into different scales. Then, Fourier spectrum based fractal descriptors is estimated at specific scales and directions to characterize the image. The support vector machine (SVM) was used to perform supervised classification. The empirical study was applied to the problem of distinguishing between normal and abnormal brain magnetic resonance images (MRI) affected with Alzheimer disease (AD). Our results demonstrate that fractal descriptors estimated in VMD domain outperform those estimated in DWT and EMD domains; and also those directly estimated from the original image. [ABSTRACT FROM AUTHOR]

In this paper, a method for on-machine tool condition monitoring by processing the turned surface images has been proposed. Progressive monitoring of cutting tool condition is inevitable to maintain product quality. Thus, image texture analyses using gray level co-occurrence matrix, Voronoi tessellation and discrete wavelet transform based methods have been applied on turned surface images for extracting eight useful features to describe progressive tool flank wear. Prediction of cutting tool flank wear has also been performed using these eight features as predictors by utilizing linear support vector machine based regression technique with a maximum 4.9% prediction error. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the effects that different preprocessing techniques have on the performance of features extracted from Haralick's co-occurrence matrix, one of the best known methods for analyzing image texture. In addition, we compare and combine different strategies for extracting descriptors from the co-occurrence matrix. We propose an ensemble of different preprocessing methods, where, for each descriptor, a given Support Vector Machine (SVM) classifier is trained. The set of classifiers is then combined by weighted sum rule. The best result is obtained by combining the extracted descriptors using the following preprocessing methods: wavelet decomposition, local phase quantization, orientation, and the Weber law descriptor. Texture descriptors are extracted from the entire co-occurrence matrix, as well as from sub-windows, and evaluated at multiple scales. We validate our approach on eleven image datasets representing different image classification problems using the Wilcoxon signed rank test. Results show that our approach improves the performance of standard methods. All source code for the approaches tested in this paper will be available at: https://www.dei.unipd.it/node/2357 [ABSTRACT FROM AUTHOR]

Interpretation of long-term Electroencephalography (EEG) records is a tiresome task for clinicians. This paper presents an efficient, low cost and novel approach for patient-specific classification of long-term epileptic EEG records. We aim to achieve this with the minimum supervision from the neurologist. To accomplish this objective, first a novel feature extraction method is proposed based on the mapping of EEG signals into two dimensional space, resulting into a texture image. The texture image is constructed by mapping and scaling EEG signals and their associated frequency sub-bands into the gray-level image domain. Image texture analysis using gray level co-occurrence matrix (GLCM) is then applied in order to extract multivariate features which are able to differentiate between seizure and seizure-free events. To evaluate the discriminative power of the proposed feature extraction method, a comparative study is performed, against other dedicated feature extraction methods. The comparative performance evaluations show that the proposed feature extraction method can outperform other state-of-art feature extraction methods with a low computational cost. With a training rate of 25%, the overall sensitivity of 70.19% and specificity of 97.74% are achieved in the classification of over 163 h of EEG records using support vector machine (SVM) classifiers with linear kernels and trained by the stochastic gradient descent (SGD) algorithm. [ABSTRACT FROM AUTHOR]

Abstract: A steganalysis algorithm based on colors-gradient co-occurrence matrix (CGCM) is proposed in this paper. CGCM is constructed with colors matrix and gradient matrix of the GIF image, and 27-dimensional statistical features of CGCM, which are sensitive to the color-correlation between adjacent pixels and the breaking of image texture, are extracted. Support vector machine (SVM) technique takes the 27-dimensional statistical features to detect hidden message in GIF images. Experimental results indicate that the proposed algorithm is more effective than Zhao''s algorithm for several existing GIF steganographic algorithms and steganography tools, especially for multibit assignment (MBA) steganography and EzStego. Furthermore, the time efficiency of the proposed algorithm is much higher than Zhao''s algorithm. [Copyright &y& Elsevier]

Facial recognition systems are widely used in various daily applications due to their high accuracy rate and ease of use. Unfortunately, their performance drops drastically when encountering spoofing attacks. In this paper, a novel pipeline for face spoofing detection based on the extraction of Heterogeneous Auto-Similarities of Characteristics (HASC) descriptor from the HSV and YCbCr color spaces is proposed. To detect print and replay spoofing attacks, the HASC descriptor encodes linear and non-linear dependencies between low-level dense features of the color face image using covariance and information-theoretic measures. The combination of covariance and information-theoretic measures makes HASC an efficient descriptor to explore how the features are interrelated in real and fake faces. The HASC texture descriptions are extracted separately from each color channel of the HSV and YCbCr color spaces, and the resulting feature vectors are concatenated into an extended feature vector to better discriminate between real and fake faces. Extensive experiments are conducted on publicly available databases namely, Replay-Attack, CASIA FASD, and MSU MFSD. The obtained results demonstrate that while being of low dimension, the HASC descriptor when fed into a binary Support Vector Machine (SVM) classifier, achieves competitive results compared to the state-of-the-art. [ABSTRACT FROM AUTHOR]

• PET / MR lung images with two types of cancer were used for the analysis. • Various methods of image texture analysis were used. • Several classification methods were used, including SVM, kNN, RF, deep learning. • Division into adenocarcinoma and squamous cell carcinoma with 75.48 % efficiency. Accurate differentiation of the histological sub-type of ono-small-cell lung cancer (NSCLC) at an early stage of diagnosis is crucial in choosing appropriate treatment option as soon as possible. Our study have been undertaken to classify two types of NSCLC – adenocarcinoma (ADC) and squamous cell carcinoma (SCC). PET / MR images from 44 patients with diagnosed adenocarcinoma (24 patients) and squamous cell carcinoma (20 patients) were used for the study. We managed to obtain 155 regions of interest with a size of 128 × 128 pixels, which were used for further analysis. 135 texture parameters were calculated, which were then used for classification using different types of classifiers. The best results (75.48 %) were achieved using the Support Vector Machines (SVM) classifier and texture parameters histogram of oriented gradients (HOG) while obtaining the highest values of specificity and sensitivity. The other results of the classification are satisfactory to a large extent. The achieved results are satisfactory and give an opportunity to develop and improve the diagnostic process of non-small cell lung tumors at the imaging stage. [ABSTRACT FROM AUTHOR]

• A texture-based damage detection method for wire ropes is proposed. • Uniform local binary pattern has been used for image texture feature extraction. • Influences of key parameters on the damage detection performance are studied. • A comparative study of three machine learning algorithms is conducted. • Support vector machine achieves better results on the new-built filtered data set. Visual method is a potential way to intuitively grasp the surface damage state of wire ropes to guarantee its reliability and safety. However, the various appearance characteristics of ropes are seriously affected by noise and effective inspection methods are lacking; therefore, efficient preprocessing and damage detection methods are urgently needed. Aiming at the influence of illumination, feature dimension, and the performance of different classification algorithms, this paper presents a novel method for the damage detection of wire ropes based on texture features, called WR-LBPML. To uniform illuminate and highlight textures of the rope images, different homomorphic filtering algorithms were compared and applied. A total of 59 texture features were extracted from rope surface images using uniform local binary patterns (u-LBP) operators. Due to the redundancy of the dataset, the features were processed using principal component analysis. Finally, we conducted a comparative study of three machine learning algorithms. Extensive experiments and comparisons of rope damage detection indicated that the proposed method can achieve optimal performance (accuracy: 93.3%, efficiency: 0.04 ms per sample) using a support vector machine with 10 features extracted by u-LBP operator, in which the images were filtered by a Gaussian low-pass filter with a block size of 1. [ABSTRACT FROM AUTHOR]